Despeckling and enhancement of ultrasonography with multiscale permutated diffusion approaches

Abstract

Ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body This imaging modality is often preferred over other medical imaging modalities because it is noninvasive portable versatile and also relatively of low cost However, the usefulness of medical ultrasonography is degraded by signal dependent noise called speckle which is multiplicative in nature Speckle is considered as the dominant source of noise in ultrasound imaging and should be processed without affecting important image features The main purposes for speckle reduction in medical ultrasound imaging are To improve the human interpretation of ultrasound images To improve the speed and accuracy of many ultrasound image processing tasks such as segmentation and registration by adopting despeckling as a preprocessing step In this work four partial differential equation based models have been proposed for despeckling and enhancement of ultrasonography Use of a second order PDE based methods tend to cause blocky effects in the image and use of a fourth order PDE requires more number of iterations to converge and also leave the processed images with isolated black and white speckles